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Gamma-ray field in air from radioactive sources uniformly distributed in the ground is calculated by numerically solving the photon transport equation. The scattered flux is developed on the Legendre polynomials basis of the spherical harmonics method and a double P1 approximation is applied. The method is implemented in the Octave programming package. The calculation of the gamma-ray field is also done by Monte Carlo simulation using an optimised geometry model of the soil-air medium that is implemented in Geant4 simulation code. The results obtained by the two methods are in good agreement. The computing time for the spherical harmonics deterministic method is significantly less than the Monte Carlo simulation. Dose rate conversion factors in the air are calculated for the natural radioactive series of 238U and 232Th and for 40K nuclide and for soils contaminated by various radionuclides. The results are in agreement with the published theoretical and experimental studies., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The computational voxel phantom 'High-Definition Reference Korean-Man (HDRK-Man)' was implemented into the Monte Carlo transport toolkit Geant4. The voxel model, adjusted to the Reference Korean Man, is 171 cm in height and 68 kg in weight and composed of ∼30 million voxels whose size is 1.981 × 1.981 × 2.0854 mm 3 The Geant4 code is then utilised to compute the dose conversion coefficients (DCCs) expressed in absorbed dose per air kerma free in air for >30 tissues and organs, including almost all organs required in the new recommendation of the ICRP 103, due to a broad parallel beam of monoenergetic photons impinging in antero-postero direction with energy ranging from 10 to 150 keV. The computed DCCs of different organs are found to be in good agreement with data published using other simulation codes. Also, the influence of patient size on DCC values was investigated for a representative body size of the adult Korean patient population. The study was performed using five different sizes covering the range of 0.8-1.2 magnification order of the original HDRK-Man. It focussed on the computation of DCC for the human heart. Moreover, the provided DCCs were used to present an analytical parameterisation for the calculation of the cardiac absorbed dose for any arbitrary X-ray spectrum and for those patient sizes. Thus, the present work can be considered as an enhancement of the continuous studies performed by medical physicist as part of quality control tests and radiation protection dosimetry., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

In this article, an approach to compute the dose conversion coefficients (DCCs) is described for the computational voxel phantom 'High-Definition Reference Korean-Man' (HDRK-Man) using artificial neural networks (ANN). For this purpose, the voxel phantom was implemented into the Monte Carlo (MC) transport toolkit GEANT4, and the DCCs for more than 30 tissues and organs, due to a broad parallel beam of monoenergetic photons with energy ranging from 15 to 150 keV by a step of 5 keV, were calculated. To study the influence of patient size on DCC values, DCC calculation was performed, for a representative body size population, using five different sizes covering the range of 80-120 % magnification of the original HDRK-Man. The focus of the present study was on the computation of DCC for the human heart. ANN calculation and MC simulation results were compared, and good agreement was observed showing that ANNs can be used as an efficient tool for modelling DCCs for the computational voxel phantom. ANN approach appears to be a significant advance over the time-consuming MC methods for DCC calculation., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

In this paper an approach to model dose distributions, isodose curves and dose uniformity in the Tunisian Gamma Irradiation Facility using artificial neural networks (ANNs) are described. For this purpose, measurements were carried out at different points in the irradiation cell using polymethyl methacrylate dosemeters. The calculated and experimental results are compared and good agreement is observed showing that ANNs can be used as an efficient tool for modelling dose distribution in the gamma irradiation facility. Monte Carlo (MC) photon-transport simulation techniques have been used to evaluate the spatial dose distribution for extensive benchmarking. ANN approach appears to be a significant advance over the time-consuming MC or the less accurate regression methods for dose mapping. As a second application, a detailed dose mapping using two different product densities was carried out. The minimum and maximum dose locations and dose uniformity as a function of the irradiated volume for each product density were determined. Good agreement between ANN modelling and experimental results was achieved.

A single-parameter geometry to describe soil is achieved for Monte Carlo calculation of absorbed dose rate in air for photon emitters from natural radionuclides. This optimised geometry based on physical assumptions consists of the soil part whose emitted radiation has a given minimum probability to reach the detector. This geometry was implemented in Geant4 toolkit and a significant reduction in computation time was achieved. Simulation tests have shown that for soil represented by a cylinder of 40 m radius and 1 m deep, >98% of the calculated dose rate conversion coefficients in air at 1 m above the ground is generated by only 6% of the soil volume in the case of uniform distribution of radioactivity, and >99.2% of the calculated dose rate for an exponential distribution. When the soil is represented by the entire optimised geometry, 99% of the conversion coefficients values are reached for a soil depth of 1 m and 100% for that of approximately 2 m.